Many electronic devices use computer vision to obtain, process, and understand the content in images captured by cameras or sensors usually to provide information to a user or make a decision depending on the content of the image. This involves using object detection, object, recognition, and/or feature extraction techniques so that a computer can identify objects in the image. One conventional technique is to use histograms of oriented gradients (HOGs) which map color and luminance gradients in an image to a number of histogram channels that are used to form a gradient distribution that forms the histogram. When certain gradient distributions are recognized in the image, this may indicate certain objects or edges of objects in the image. HOGs, however, often are formed by using a very high number of complicated computations and memory transactions resulting in a high power consumption that is difficult for high-end, high power electronic devices to maintain and makes it practically impossible for low-end or low-power devices to use (such as smartphones or wearable devices). This is especially true when power consuming features for forming the HOGs are desired such as an always-on mode.